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Record W3191850379 · doi:10.1111/ipd.12891

Association between psychosocial determinants of adverse childhood experiences and severe early childhood caries among First Nations children

2021· article· en· W3191850379 on OpenAlexafffundabout
Wan Ting Tsai, Herenia P. Lawrence

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsToronto Public HealthPublic Health OntarioUniversity of Toronto
FundersInstitute of Aboriginal Peoples Health
KeywordsMedicinePsychosocialOvercrowdingLogistic regressionVulnerability (computing)Odds ratioSocial supportLonelinessEnvironmental healthPsychiatryDemographyPsychology

Abstract

fetched live from OpenAlex

AIM: To determine whether psychosocial determinants of adverse childhood experiences (ACE), from pregnancy to 2 years old, are associated with severe early childhood caries (S-ECC) in Indigenous children. DESIGN: Secondary data analyses from an ECC prevention trial among 344 First Nations mother-child dyads living on- and off-reserve in Ontario and Manitoba, Canada. Stratified (on-/off-reserve) logistic regression, controlling for mother's age and income source, assessed three categories of psychosocial ACE determinants: alcohol/drug misuse, household financial hardship (overcrowding and food insecurity) and emotional/social well-being (Perceived Stress Scale (PSS-14), sense of personal control (SOC), social support, subjective social status). RESULTS: Household overcrowding [adjusted odds ratio (AOR) = 1.89 (95% CI: 1.06-3.38)], food insecurity [AOR = 2.86 (1.53-5.34)] and mothers' high perceived stress [AOR = 2.48 (1.40-4.37)] were associated with S-ECC (dmft > 9) for those on-reserve. Maternal SOC had a protective effect for off-reserve children [AOR = 0.17 (0.03-0.95)]. CONCLUSIONS: Increased efforts to reduce psychosocial ACE determinants are paramount to decreasing Indigenous children's vulnerability to S-ECC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.277
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2021
Admission routes3
Has abstractyes

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